# Network-based statistical methods to decode interactions within microbiomes

> **NIH NIH R01** · OREGON STATE UNIVERSITY · 2020 · $187,534

## Abstract

This project will innovate statistical methods that resolve how the myriad microorganisms that comprise the
human microbiome interact with one another and with their host. The human microbiome is an important
contributor to health and physiology, but efforts to manage it are stifled by a limited understanding of how
the microbiome operates. One common strategy towards resolving this operation is to use correlation-type
analyses of organismal abundance to model the biological interactions among microbes or between
microbes and their host. However, the underlying biological interactions are often masked by the
co-occurrence patterns in a community: two microbes that independently interact with a third, but not with
one another, may appear to correlate. Additionally, existing methods fail to account for specific properties of
microbiome data, including its heterogeneous compositional count nature, the complex environmental
context, and its evolutionary structure. This project will develop statistical methods built on conditional
dependencies that disentangle biological interactions from marginal correlations to produce mechanistically
and evolutionarily relevant network models of how microbes interact with one another and their host.
Specifically, it will (1) establish statistical methods that incorporate unique features in microbiome data to
detect biological interactions, (2) advance graphical models for data integration to estimate how microbiota
interact with paired 'omics data (e.g., metabolomics) and infer the phylogenetic redundancy of these
interactions, and (3) create a new statistical regularization framework to quantify how host variation impacts
the topology of microbial interaction networks. The methods and software produced by this work will
transform our understanding of how microbiomes operate and influence or respond to their host. As a result,
this work will produce knowledge critical to long-term efforts to manage and engineer microbiomes with the
goal of eliciting specific clinical or physiological processes. This work can also profoundly influence industry
efforts to develop microbiome-related products such as clinical diagnostics, novel drugs, and therapeutic
probiotics.

## Key facts

- **NIH application ID:** 9985148
- **Project number:** 5R01GM126549-04
- **Recipient organization:** OREGON STATE UNIVERSITY
- **Principal Investigator:** Yuan Jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $187,534
- **Award type:** 5
- **Project period:** 2017-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985148

## Citation

> US National Institutes of Health, RePORTER application 9985148, Network-based statistical methods to decode interactions within microbiomes (5R01GM126549-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9985148. Licensed CC0.

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